2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)最新文献_第9页

Machine Learning Prediction for Phase-less Millimeter-Wave Beam Tracking 无相毫米波波束跟踪的机器学习预测

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-06-06 DOI: 10.1109/spawc51304.2022.9833935

B. Domae, Veljko Boljanovic, Rui Li, D. Cabric

{"title":"Machine Learning Prediction for Phase-less Millimeter-Wave Beam Tracking","authors":"B. Domae, Veljko Boljanovic, Rui Li, D. Cabric","doi":"10.1109/spawc51304.2022.9833935","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833935","url":null,"abstract":"Future wireless networks may operate at millimeter-wave (mmW) and sub-terahertz (sub-THz) frequencies to enable high data rate requirements. While large antenna arrays are critical for reliable communications at mmW and sub-THz bands, these antenna arrays would also mandate efficient and scalable initial beam alignment and link maintenance algorithms for mobile devices. Low-power phased-array architectures and phaseless power measurements due to high frequency oscillator phase noise pose additional challenges for practical beam tracking algorithms. Traditional beam tracking protocols require exhaustive sweeps of all possible beam directions and scale poorly with high mobility and large arrays. Compressive sensing and machine learning designs have been proposed to improve measurement scaling with array size but commonly degrade under hardware impairments or require raw samples respectively. In this work, we introduce a novel long short-term memory (LSTM) network assisted beam tracking and prediction algorithm utilizing only phase-less measurements from fixed compressive codebooks. We demonstrate comparable beam alignment accuracy to state-of-the-art phase-less beam alignment algorithms, while reducing the average number of required measurements over time.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130180532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation 扇区内压缩毫米波信道估计的结构化传感矩阵设计

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-05-23 DOI: 10.1109/spawc51304.2022.9833949

H. Masoumi, Nitin Jonathan Myers, G. Leus, S. Wahls, M. Verhaegen

引用次数: 1

CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks 查尔斯:无线网络上的信道质量自适应空中联合学习

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-05-19 DOI: 10.48550/arXiv.2205.09330

Jiayu Mao, Haibo Yang, Pei-Chen Qiu, Jia Liu, A. Yener

引用次数: 4

Serving Federated Learning and Non-Federated Learning Users: A Massive MIMO Approach 服务联邦学习和非联邦学习用户:大规模MIMO方法

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-05-17 DOI: 10.1109/SPAWC51304.2022.9833955

M. Farooq, Thanh Tung Vu, H. Ngo, Le-Nam Tran

引用次数: 2

Performance Analysis of Irregular Repetition Slotted Aloha with Multi-Cell Interference 多小区干扰下不规则重复开槽Aloha性能分析

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-05-14 DOI: 10.1109/SPAWC51304.2022.9833941

C. Srivatsa, C. Murthy

引用次数: 1

Virtually Full-duplex Cell-Free Massive MIMO with Access Point Mode Assignment 几乎全双工无蜂窝大规模MIMO与接入点模式分配

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-05-13 DOI: 10.48550/arXiv.2205.06403

M. Mohammadi, Thanh Tung Vu, B. N. Beni, H. Ngo, M. Matthaiou

引用次数: 6

Channel Estimation in RIS-assisted Downlink Massive MIMO: A Learning-Based Approach ris辅助下行大规模MIMO的信道估计:一种基于学习的方法

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-05-11 DOI: 10.48550/arXiv.2205.05577

Thanh Tung Vu, Trinh Van Chien, Canh T. Dinh, H. Ngo, M. Matthaiou

{"title":"Channel Estimation in RIS-assisted Downlink Massive MIMO: A Learning-Based Approach","authors":"Thanh Tung Vu, Trinh Van Chien, Canh T. Dinh, H. Ngo, M. Matthaiou","doi":"10.48550/arXiv.2205.05577","DOIUrl":"https://doi.org/10.48550/arXiv.2205.05577","url":null,"abstract":"For downlink massive multiple-input multiple-output (MIMO) operating in time-division duplex protocol, users can de-code the signals effectively by only utilizing the channel statistics as long as channel hardening holds. However, in a reconfigurable intelligent surface (RIS)-assisted massive MIMO system, the propagation channels may be less hardened due to the extra random fluctuations of the effective channel gains. To address this issue, we propose a learning-based method that trains a neural network to learn a mapping between the received downlink signal and the effective channel gains. The proposed method does not require any downlink pilots and statistical information of interfering users. Numerical results show that, in terms of mean-square error of the channel estimation, our proposed learning-based method outperforms the state-of-the-art methods, especially when the light-of-sight (LoS) paths are dominated by non-LoS paths with a low level of channel hardening, e.g., in the cases of small numbers of RIS elements and/or base station antennas.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124737779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Human Tracking with mmWave Radars: a Deep Learning Approach with Uncertainty Estimation 用毫米波雷达跟踪人体:一种不确定性估计的深度学习方法

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-05-06 DOI: 10.1109/spawc51304.2022.9833987

Jacopo Pegoraro, M. Rossi

{"title":"Human Tracking with mmWave Radars: a Deep Learning Approach with Uncertainty Estimation","authors":"Jacopo Pegoraro, M. Rossi","doi":"10.1109/spawc51304.2022.9833987","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833987","url":null,"abstract":"mmWave radars have recently gathered significant attention as a means to track human movement within indoor environments. Widely adopted Kalman filter tracking methods experience performance degradation when the underlying movement is highly non-linear or presents long-term temporal dependencies. As a solution, in this article we design a convolutional-recurrent Neural Network (NN) that learns to accurately estimate the position and the velocity of the monitored subjects from high dimensional radar data. The NN is trained as a probabilistic model, utilizing a Gaussian negative log-likelihood loss function, obtaining explicit uncertainty estimates at its output, in the form of time-varying error covariance matrices. A thorough experimental assessment is conducted using a 77 GHz FMCW radar. The proposed architecture, besides allowing one to gauge the uncertainty in the tracking process, also leads to greatly improved performance against the best approaches from the literature, i.e., Kalman filtering, lowering the average error against the ground truth from 32.8 to 7.59 cm and from 56.8 to 14 cm/s in terms of position and velocity tracking, respectively.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125501257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Sparse Large-Scale Fading Decoding in Cell-Free Massive MIMO Systems 无小区大规模MIMO系统中的稀疏大规模衰落解码

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-05-05 DOI: 10.48550/arXiv.2205.02733

Shuaifei Chen, Jiayi Zhang, Emil Björnson, Ozlem Tugfe Demir, B. Ai

引用次数: 3

On the Level Crossing Rate of Fluid Antenna Systems 流体天线系统的平交速率研究

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-05-03 DOI: 10.48550/arXiv.2205.01711

Priyadarshi Mukherjee, C. Psomas, I. Krikidis

引用次数: 11